Question

问题说明：csv中的数据由两列组成——日期和产品数据：

  Date                  Prod
  1/2/2018  7:43:00 PM     A
  1/1/2018  11:41:00 AM    B
  1/1/2018  7:57:00 AM     C
  1/2/2018  1:56:00 PM     A
  1/5/2018  3:29:00 AM     A
  1/3/2018  7:23:00 AM     C
  1/3/2018  1:26:00 PM     B
  1/5/2018  2:08:00 AM     A
  1/5/2018  3:47:00 PM     B

我需要返回两个json数据，以product为key，value应该是product的频率

每周一次
每年一次

喜欢：

[{"A":{"Week1":"3","Week2":"3","Week3":"5",...},{"B":{"Week1":"5","Week2":"7","Week3":"4",...},{"C":{...}}]
[{"A":{"2018":"3","2019":"3","2020":"5",...},{"B":{"2018":"5","2019":"7","2020":"4",...},{"C":{...}}]

我试过了：

df['Date'] = pd.to_datetime(df['Date'])

weekly_series = df.groupby(pd.Grouper(key='Date', freq='W'))['Date'].count()

weekly_series.index = weekly_series.index.week

Answer 1

准备你的数据框：

df["Date"] = pd.to_datetime(df["Date"])
df["Year"] = df["Date"].dt.isocalendar().year
df["Week"] = df["Date"].dt.isocalendar().week

创建您的字典：

dyear = df.groupby("Prod")[["Year"]] \
          .apply(lambda x: x.value_counts("Year").to_dict()).to_dict()

dweek = df.groupby("Prod")[["Week"]] \
          .apply(lambda x: x.value_counts("Week").add_prefix("Week").to_dict()).to_dict()

>>> dyear
{'A': {2018: 4}, 'B': {2018: 3}, 'C': {2018: 2}}

>>> dweek:
{'A': {'Week1': 4}, 'B': {'Week1': 3}, 'C': {'Week1': 2}}

在python中分类后每周和每年对数据进行分组

1 个答案: